Acting as an umbrella term for various research methods and analytical tools, data science is inclusive of several fields of study. Most of these fields employ scientific and mathematical reasoning, as well as a knowledge of computers and technology, in order to gain insights from the collection of information and data. Statistical analysis software is commonly used within data science in order to process data using statistical theories and formulas. Many data science tools perform various forms of statistical analysis, such as regressions, descriptive statistics, and other functions, in order to make predictions and analyze trends.
As a subset of mathematics which is known for collecting and analyzing data, there is a clear overlap between statistics and data science. While statisticians apply their knowledge of statistics to research studies and populations, it is more common for data scientists to use statistics in order to learn more about a specific dataset or tool. Still, there is a multitude of reasons why data scientists use statistical analysis software, which is reflected in the number of statistics-based products that have become popular within the field. This article unpacks why data scientists use statistical analysis software as well as some of the top tools that you can use to perform statistical analyses, as well as how to learn more about them.
Why use Statistical Analysis Software for Data Science?
In differentiating statistical analysis software from other types of data science tools, there are many reasons why data scientists choose to employ statistical functions in their work. Especially when engaging in projects that are focused on the collection of large stores of data there are specific rules, or theories, that were conceptualized within the field of statistics to ensure the accuracy of performing a successful data analysis. Within the field of statistics, there are several rules around the concept of statistical significance and sample size.
Statistical significance is used to assess the validity of a hypothesis when performing a study, and the proper analysis of data in statistics requires specific numbers of data entries in order to meet significance. When using statistical analysis software, there are checks in place within the software to ensure that these standards are being met, which also ensures that the research produced from your analysis is accurate and verifiable. Many of the most common methods of analyzing data are statistical methods. Exploring a dataset requires knowledge of descriptive statistics in order to find averages, means, and other information about how the data can be interpreted.
The focus on making predictions and forecasting in data science is also a reflection of statistical theories of probability. By utilizing formulas and theories which help researchers and industry professionals determine what could happen, both statistical and data analysis software can be used to uncover information about what is going on within a dataset, as well as what those findings could mean for future trends. Many students and professionals with a background in data science have the potential to find employment in statistics careers, and vice versa, depending on the software and programs that they learn to use.
Top 5 Statistical Analysis Software for Data Scientists
In making the move between statistical and data analysis, there are many overlaps between the types of programs and software that a data scientist can use. Each of the statistical analysis software products below can be used to perform various types of mathematical functions, modeling, and data analyses that are not only useful for data science students and professionals, but also statisticians, engineers, and other math and science-focused professions.
1. SAS/STAT
Known as one of the oldest and most trusted statistical analysis software programs, SAS/STAT is one of the best options for data scientists that want to perform higher-level statistical analyses or modeling. SAS Visual Statistics is a product created for statisticians and data scientists, which can be used to explore, analyze, and create predictive models for a dataset. In addition, while many programs are focused on the analysis of big data projects, SAS/STAT has the scalability for small data sets as well, ensuring that no data is too small for this powerful data science tool.
2. IBM SPSS
Statistical Package for the Social Sciences, or SPSS is one of the most commonly used statistical analysis software programs of its time. SPSS is used within social science research and is a staple within government, healthcare, and academic institutions, as well as research labs because of its effectiveness at maintaining and measuring survey data for large-scale and/or longitudinal studies. However, data scientists across industries can utilize SPSS in order to run statistical functions and visualize data.
3. Stata
Stata is another example of statistical analysis software that is commonly used within research environments, but that has multiple uses for data scientists. With features that include data management, visualization, and reporting in conjunction with statistical analysis, Stata is a one-stop shop for completing any data science project. This versatile software is compatible with programming languages like Python and SQL, making it easy for data scientists to customize and configure their data within this software.
4. MiniTab
Gaining popularity within classrooms across the globe, MiniTab was developed by researchers as a statistical processing program for students. After multiple expansions and acquisitions, MiniTab now offers several products and data science tools for different industries and environments. Not only useful to data scientists and statisticians within research universities, this dynamic software has also gained traction within the corporate world and has affiliations with several large companies and clients. The MiniTab statistical software is accessible to users of all levels and backgrounds, with the capabilities to not only perform statistical analysis but to create engaging data visualizations and models.